DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
A reply was filed on 05/06/2026. The amendments to the specification and claims have been entered. Claims 10-16 and 19-31 are pending in the application and examined herein.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Rejections - 35 USC § 112(b)
Claims 10-16 and 19-31 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 10 recites “determining, using a virtual sensor comprising analytic functions derived from a computational fluid dynamics simulation of mixed-gas heat transfer within the canister and temporal-spatial temperature data validated in scale-model canister leakage experiments”. This phrase appears to be incomplete. It is unclear what is “determin[ed]” in the claimed manner.
Claim 10 recites “a virtual sensor value representing a change in a bottom-to-top canister temperature difference”. This phrase would also appear to be incomplete. It is unclear the relationship between the “virtual sensor value” and the remaining steps recited in the claim. Additionally, it is unclear the relationship between the “bottom-to-top canister temperature difference” and the “first external surface of a plurality of external surfaces of the canister”, “second external surface of the plurality of external surface of the canister”, “a first temperature difference”, and “a second temperature difference”. This further renders unclear the relationship between the “bottom-to-top” and the “first superior end” and “inferior end” recited in claim 12 and the “first end” and “second end” recited in claim 15. It is also unclear the relationship between the “bottom-to-top canister temperature difference” and the “temperature differences” recited in claim 16.
Claim 25 is indefinite because it is unclear the relationship between the “temperatures”, “plurality of exterior surface locations”, “top region”, “bottom region’, and “bottom-to-top exterior surface temperature difference”. This further renders unclear the relationship between these features and the “top-center exterior surface location” and the “bottom-center exterior surface location” recited in claim 27.
Claim 25 is further indefinite because it is unclear the relationship between the “internal gas composition” and the “baseline gas composition”. This further renders unclear the relationship between these features and the “gaseous moiety” and “air mixture concentration” recited in claim 28.
Claim 26 is indefinite because it is unclear if the “bottom-to-top exterior surface temperature difference” is referring to the same features as the “bottom-to-top exterior surface temperature difference” previously recited in parent claim 25, or another feature. Additionally, parent claim 25 previously recites “automatically generating an alarm when the virtual sensor output indicates a deviation from a baseline gas composition indicative of canister leakage”. It is unclear the relationship between this feature and the “wherein the alarm is generated when a bottom-to-top exterior surface temperature difference exceeds a threshold”. For example, it is unclear if the temperature difference exceeding the threshold is the same as the virtual sensor output indicating a deviation from a baseline gas composition, if the claim requires generating an alarm both when the virtual sensor output indicates a deviation and when the temperature difference exceeds the threshold, or another interpretation.
Any claim not explicitly addressed above is rejected because it is dependent on a rejected base claim.
Claim Rejections - 35 USC § 103
Claims 10-16 and 19-31 are rejected under 35 U.S.C. 103 as being unpatentable over JP Publication No. 2020-148769 (“Takeda”) in view of “Virtual Sensors” (“Martin”) and “Thermal modeling of a vertical dry storage cask for used nuclear fuel” (“Li”).
Regarding claim 10, Takeda (previously cited) (see FIGS. 1, 13-15) discloses a method for monitoring stored spent fuel rods (“spent fuel”) contained in a canister (4) ([0001], [0036]), the method comprising:
measuring, by a first physical sensor (e.g., 13A) at a first time, a first temperature (e.g., TB) of a first external surface (e.g., 4B) of a plurality of external surfaces (4B, 4T, 4S) of the canister ([0049], [0051], [0173]);
measuring, by a second physical sensor (e.g., 13B) at the first time, a second temperature (e.g., TT) at a second external surface (e.g., 4T) of the plurality of external surfaces of the canister ([0049], [0051], [0173]);
measuring, by the first physical sensor at a second time subsequent to the first time, a third temperature at the first external surface ([0049], [0051], [0063], [0069]);
measuring, by the second physical sensor at the second time, a fourth temperature at the second external surface ([0049], [0051], [0063], [0069]);
determining a first temperature difference (e.g., ΔTBT) between the second temperature and the first temperature ([0048], [0050], [0173]);
determining a second temperature difference between the third temperature and the fourth temperature ([0048], [0050], [0173]);
determining a value representing a change in a bottom-to-top canister temperature difference over a time interval between the first time and the second time based on (1) a difference between the first temperature difference and the second temperature difference and (2) a difference between the second time and the first time ([0048], [0050], [0063], [0070], [0173]); and
automatically triggering an alarm, responsive to a determination that the value exceeds a threshold value ([0072]).
Takeda does not appear to disclose the value is determined using a virtual sensor as recited in claim 10.
Martin (previously cited) (see FIG. 2) is similarly directed towards a method for monitoring a system (“asset”) using physical sensors (“physical sensors”, “PS”) (p. 315: “the condition of the physical world can either be ‘directly’ observed (by a physical sensor) or indirectly derived by fusing data from one or more physical sensors, i.e., applying virtual sensors”; p. 317: “An asset describes an object, subject, or system which, as a whole or in parts, is to be monitored or observed”). Martin teaches using a virtual sensor (“virtual sensors”, “VS”) including a set of analytic functions determined by a simulation and temporal-spatial temperature data and determining a virtual sensor value (“virtual sensor data”) based on sensor information from the physical sensors (p. 315: “the condition of the physical world can either be ‘directly’ observed (by a physical sensor) or indirectly derived by fusing data from one or more physical sensors, i.e., applying virtual sensors”, “software-based virtual sensors offer an additional abstraction layer built on digital representations of sensor hardware. They issue signals that aggregate input from physical sensors”; p. 317: “more complex, but still simple fusion functions apply methods such as scaling, filtering, linearization, aggregation, extrapolation and others to the source data in order to provide a final measurement result”; pp. 317-318: “machine learning-based functions are applicable, which are able to infer a target of interest from data sources of different resolution, availability, type and form”; p. 318: “a digital twin represents an asset’s virtual counterpart that can be leveraged to digitally mirror and constantly manage it. It combines and integrates an asset’s data sources and controls its availability and validity”, “The derived measurements produced by a data fusion function represent the virtual sensor data”, “virtual sensors can serve both as data sources for digital twins as well as their integrators, since a digital twin is also an integral part of the virtual sensor concept”).
Martin further teaches the virtual sensor technique provides the advantages of more precise measurements, reducing signal noise, and increasing reliability (p. 317: “fusion enables both more precise measurements of one specific phenomenon (e.g., temperature at a specific location within a system)”; pp. 318-319: “overall accuracy may increase, and at the same time uncertainty as well as transmission volume is reduced.... Multiple sensors providing redundant information can also increase reliability in the event of a sensor failure or malfunction.... Furthermore, the influence of drifts caused by sensor accuracy can be detected and optionally corrected”). It would have therefore been obvious to a person having ordinary skill in the art before the effective filing date (“POSA”) to employ Martin’s virtual sensor and virtual sensor value technique in Takeda’s method for the benefits thereof. Thus, modification of Takeda in order to enhance precision and reliability, as suggested by Martin, would have been obvious to a POSA.
Martin teaches using the virtual sensor including a set of analytic functions determined by simulation and temporal-spatial temperature data and determining the virtual sensor value based on sensor information from the physical sensors, as discussed above, but does not appear to teach the simulation is a computational fluid dynamics simulation of mixed-gas heat transfer within the canister or validating the data by scale-model canister leakage experiments.
However, it was known in the art to simulate thermal and leakage conditions in a spent fuel canister using computational fluid dynamics simulations of mixed-gas heat transfer within the canister and to validate temporal-spatial temperature data using scale-model canister leakage experiments for monitoring spent fuel canisters. For example, Li (newly cited) is similarly directed towards a method for monitoring a spent fuel canister for leaks by determining a change in a bottom-to-top canister temperature difference (Abstract, pp. 85-86: “for condition monitoring of helium integrity in welded canisters during storage, the key parameter of interest is the difference between the surface temperatures at the top and bottom of the canister (i.e., ΔTBT).... The change in the fill-gas condition inside a canister (e.g., due to helium leakage) can be detected by monitoring ΔTBT, which has been shown to increase after canister helium depressurization (i.e., leakage)”). Li teaches determining the change in the bottom-to-top canister temperature difference using a computational fluid dynamics simulation (“ANSYS/FLUENT”) of mixed-gas heat transfer within the canister and temporal-spatial temperature data validated in scale-model canister leakage experiments (p. 86: “The change in the fill-gas condition inside a canister (e.g., due to helium leakage) can be detected by monitoring ΔTBT, which has been shown to increase after canister helium depressurization (i.e., leakage) ... in a mockup cask electrically heated to 22.6 kW”, “The ANSYS/Fluent software can simulate the thermal behaviors and capture the important features of a vertical storage cask”, “The latest development, the “Remote Area Modular Monitoring (RAMM)” system, appears promising for DCSS designs in general and vertical dry storage casks in particular, on the basis of the 3D simulation results”; p. 87: “The thermal performance of a vertical dry storage casks with a welded canister containing high-burnup fuel has been studied, using the ANSYS/FLUENT code simulating heat transfer and gas flow, in a 3D model of a vertical storage cask”, “The validation results showed reasonably good agreement between the calculated and measured canister axial surface temperatures”, “The validation of the results of the ANSYS/FLUENT simulation against the data, coupled with the experiment and additional insights gained from the validation exercise and blockage simulation, are important to future simulation and analysis of the thermal performance of vertical dry storage casks—particularly for monitoring cask conditions and performance as part of aging management during extended long-term storage at the ISFSIs”).
Li further teaches the computational fluid dynamics simulation of mixed-gas heat transfer within the canister provides the advantages of simulating changes in heat load and producing accurate temperature profiles (p. 86: “The ANSYS/Fluent software can simulate the thermal behaviors and capture the important features of a vertical storage cask. Based on the validation results, it is indicated that a reasonable tighter convergence criteria is needed to obtain a more accurate temperature profiles and thus peak temperatures, yet in an acceptable simulation time cost”, “once the model for the dry storage cask is constructed, conducting 3D simulations for the change in the cask heat load is straightforward”; p. 87: “The thermal performance of a vertical dry storage casks with a welded canister containing high-burnup fuel has been studied, using the ANSYS/FLUENT code simulating heat transfer and gas flow, in a 3D model of a vertical storage cask”, “The validation results showed reasonably good agreement between the calculated and measured canister axial surface temperatures. For the ANSYS/FLUENT simulations, the results showed that a tighter convergence criterion yielded slightly better agreement with the data, but that improvement could also be obtained by adjusting the ambient temperature value assumed in the simulation. The validation of the results of the ANSYS/FLUENT simulation against the data, coupled with the experiment and additional insights gained from the validation exercise and blockage simulation, are important to future simulation and analysis of the thermal performance of vertical dry storage casks—particularly for monitoring cask conditions and performance as part of aging management during extended long-term storage at the ISFSIs”) and validating the data provides the advantage of lending credence to the simulations (p. 84: “Validation of thermal analyses is important to lend credence to the computer simulations. The focus in this section is on validating the thermal analysis of a vertical dry storage cask against the temperature measurement data and the results obtained by others in thermal modeling of a HI-STORM 100 storage cask”). It would have therefore been obvious to a POSA to include the computational fluid dynamics simulation of mixed-gas heat transfer within the canister and validation using scale-model canister leakage experiments, as taught by Li, in the modified Takeda’s method for the predictable purpose of enhancing modeling, accuracy, and reliability as suggested by Li.
Regarding claim 11, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda does not appear to disclose a specific value of the predetermined threshold value. However, it would have been obvious to a POSA to have a predetermined threshold value that is two degrees Celsius since it has been held that, where the general conditions of a claim are disclosed in the prior art, discovering an optimum or workable range involves only routine skill in the art. Takeda explicitly discloses the predetermined threshold value may be determined via simulations ([0047]).
Regarding claims 12 and 15, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses the first external surface is located at a first superior end (4B) of the canister and the second external surface is located at an inferior end (4T) of the canister (FIGS. 1-2, 13-15, [0048]).
Regarding claim 13, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses determining, responsive to the determination that the virtual sensor value exceeds the threshold value, an indication of a chemical change of the spent fuel rods, the chemical change comprising an increase in gaseous moiety and air mixture concentrations ([0052]-[0054], [0056]).
Regarding claim 14, Takeda in view of Martin and Li teaches the method as recited in claim 13. Takeda discloses the moiety is an element selected from the group consisting of helium, krypton, xenon, hydrogen, nitrogen, argon and combinations thereof ([0052]-[0054]).
Regarding claim 16, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses reviewing a database correlating change in temperature differences with an algorithm-derived time line ([0046], [0067]).
Regarding claim 19, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses determining, responsive to the determination that the virtual sensor value exceeds the threshold value, an indication of a pressure decrease inside the canister (FIG. 22, [0052], [0057], [0066], [0073], [0153]-[0154], [0163]).
Regarding claim 20, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses determining, responsive to the determination that the virtual sensor value exceeds the threshold value, that the stored spent fuel rods are damaged ([0063], [0172]-[0173]).
Regarding claim 21, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses the canister is oriented vertically and a height of the canister is greater than a width of the canister (FIG. 1, [0033], [0139]).
Regarding claim 22, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses monitoring, by a sensor monitor module (18), a plurality of physical sensors (13A, 13B, 13C, 13E), which include the first physical sensor and the second physical sensor, configured to measure at least one of the first temperature, the second temperature, the third temperature, or the fourth temperature (FIG. 3, [0050], [0068]-[0069]).
Regarding claim 23, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses the difference between the second time and the first time is 60 seconds (FIGS. 13-15).
Regarding claim 24, Takeda in view of Martin and Li teaches the method as recited in claim 10. Takeda discloses receiving, by a communication module (16a), sensor information from a sensor monitor module (18), the sensor monitor module in communication with the first physical sensor and the second physical sensor and receiving the sensor information therefrom; and receiving, by a data server (17), the sensor information from the communication module (FIG. 3, [0068]-[0069]).
Regarding claim 25, Takeda (see FIGS. 1, 13-15) discloses a method for detecting gas leakage in a sealed spent-fuel canister, comprising:
measuring temperatures (TB, TT, TS) at a plurality of exterior surface locations (4B, 4T, 4S) of the canister including a top region and a bottom region ([0049], [0051], [0063], [0069], [0173]);
generating, a sensor output that correlates changes in a bottom-to-top exterior surface temperature difference with an internal gas composition of the canister ([0048], [0050], [0052]-[0054], [0056], [0063], [0070], [0173]); and
automatically generating an alarm when the sensor output indicates a deviation from a baseline gas composition indicative of canister leakage ([0072]).
Takeda does not appear to disclose the sensor output is a virtual sensor output generated by a virtual sensor as recited in claim 25.
Martin (see FIG. 2) is similarly directed towards a method for monitoring a system (“asset”) using physical sensors (“physical sensors”, “PS”) (p. 315: “the condition of the physical world can either be ‘directly’ observed (by a physical sensor) or indirectly derived by fusing data from one or more physical sensors, i.e., applying virtual sensors”; p. 317: “An asset describes an object, subject, or system which, as a whole or in parts, is to be monitored or observed”). Martin teaches generating, by a virtual sensor (“virtual sensors”, “VS”) executed on an edge-computing monitoring system, a virtual sensor output (“virtual sensor data”), wherein the virtual sensor output is computed using analytic functions obtained by a simulation and temporal-spatial temperature data (p. 315: “the condition of the physical world can either be ‘directly’ observed (by a physical sensor) or indirectly derived by fusing data from one or more physical sensors, i.e., applying virtual sensors”, “software-based virtual sensors offer an additional abstraction layer built on digital representations of sensor hardware. They issue signals that aggregate input from physical sensors”; p. 317: “more complex, but still simple fusion functions apply methods such as scaling, filtering, linearization, aggregation, extrapolation and others to the source data in order to provide a final measurement result”; pp. 317-318: “machine learning-based functions are applicable, which are able to infer a target of interest from data sources of different resolution, availability, type and form”; p. 318: “a digital twin represents an asset’s virtual counterpart that can be leveraged to digitally mirror and constantly manage it. It combines and integrates an asset’s data sources and controls its availability and validity”, “The derived measurements produced by a data fusion function represent the virtual sensor data”, “virtual sensors can serve both as data sources for digital twins as well as their integrators, since a digital twin is also an integral part of the virtual sensor concept”).
Martin further teaches the virtual sensor technique provides the advantages of more precise measurements, reducing signal noise, and increasing reliability (p. 317: “fusion enables both more precise measurements of one specific phenomenon (e.g., temperature at a specific location within a system)”; pp. 318-319: “overall accuracy may increase, and at the same time uncertainty as well as transmission volume is reduced.... Multiple sensors providing redundant information can also increase reliability in the event of a sensor failure or malfunction.... Furthermore, the influence of drifts caused by sensor accuracy can be detected and optionally corrected”). It would have therefore been obvious to a POSA to employ Martin’s virtual sensor technique in Takeda’s method for the benefits thereof. Thus, modification of Takeda in order to enhance precision and reliability, as suggested by Martin, would have been obvious to a POSA.
Martin teaches the virtual sensor output is computed using analytic functions obtained by simulation and temporal-spatial temperature data, as discussed above, but does not appear to teach the simulation is a computational fluid dynamics simulation of mixed-gas thermal-hydraulics of the canister or using data obtained from scale-model canister leakage experiments.
However, it was known in the art to simulate thermal and leakage conditions in a spent fuel canister using computational fluid dynamics simulations of mixed-gas thermal-hydraulics of the canister and to use temporal-spatial temperature data obtained from scale-model canister leakage experiments for monitoring spent fuel canisters. For example, Li is similarly directed towards a method for detecting gas leakage in a sealed spent-fuel canister by determining a change in a bottom-to-top canister temperature difference correlated with an internal gas composition of the canister (Abstract, pp. 85-86: “for condition monitoring of helium integrity in welded canisters during storage, the key parameter of interest is the difference between the surface temperatures at the top and bottom of the canister (i.e., ΔTBT).... The change in the fill-gas condition inside a canister (e.g., due to helium leakage) can be detected by monitoring ΔTBT, which has been shown to increase after canister helium depressurization (i.e., leakage)”). Li teaches determining the change in the bottom-to-top canister temperature difference using a computational fluid dynamics simulation (“ANSYS/FLUENT”) of mixed-gas thermal-hydraulics of the canister and temporal-spatial temperature data obtained from scale-model canister leakage experiments (p. 86: “The change in the fill-gas condition inside a canister (e.g., due to helium leakage) can be detected by monitoring ΔTBT, which has been shown to increase after canister helium depressurization (i.e., leakage) ... in a mockup cask electrically heated to 22.6 kW”, “The ANSYS/Fluent software can simulate the thermal behaviors and capture the important features of a vertical storage cask”, “The latest development, the “Remote Area Modular Monitoring (RAMM)” system, appears promising for DCSS designs in general and vertical dry storage casks in particular, on the basis of the 3D simulation results”; p. 87: “The thermal performance of a vertical dry storage casks with a welded canister containing high-burnup fuel has been studied, using the ANSYS/FLUENT code simulating heat transfer and gas flow, in a 3D model of a vertical storage cask”, “The validation results showed reasonably good agreement between the calculated and measured canister axial surface temperatures”, “The validation of the results of the ANSYS/FLUENT simulation against the data, coupled with the experiment and additional insights gained from the validation exercise and blockage simulation, are important to future simulation and analysis of the thermal performance of vertical dry storage casks—particularly for monitoring cask conditions and performance as part of aging management during extended long-term storage at the ISFSIs”).
Li further teaches the computational fluid dynamics simulation of mixed-gas thermal-hydraulics of the canister provides the advantages of simulating changes in heat load and producing accurate temperature profiles (p. 86: “The ANSYS/Fluent software can simulate the thermal behaviors and capture the important features of a vertical storage cask. Based on the validation results, it is indicated that a reasonable tighter convergence criteria is needed to obtain a more accurate temperature profiles and thus peak temperatures, yet in an acceptable simulation time cost”, “once the model for the dry storage cask is constructed, conducting 3D simulations for the change in the cask heat load is straightforward”; p. 87: “The thermal performance of a vertical dry storage casks with a welded canister containing high-burnup fuel has been studied, using the ANSYS/FLUENT code simulating heat transfer and gas flow, in a 3D model of a vertical storage cask”, “The validation results showed reasonably good agreement between the calculated and measured canister axial surface temperatures. For the ANSYS/FLUENT simulations, the results showed that a tighter convergence criterion yielded slightly better agreement with the data, but that improvement could also be obtained by adjusting the ambient temperature value assumed in the simulation. The validation of the results of the ANSYS/FLUENT simulation against the data, coupled with the experiment and additional insights gained from the validation exercise and blockage simulation, are important to future simulation and analysis of the thermal performance of vertical dry storage casks—particularly for monitoring cask conditions and performance as part of aging management during extended long-term storage at the ISFSIs”) and using data obtained from scale-model canister leakage experiments provides the advantage of validating the simulations and calculations, thereby lending credence to the simulations (p. 84: “Validation of thermal analyses is important to lend credence to the computer simulations. The focus in this section is on validating the thermal analysis of a vertical dry storage cask against the temperature measurement data and the results obtained by others in thermal modeling of a HI-STORM 100 storage cask”). It would have therefore been obvious to a POSA to include the computational fluid dynamics simulation of mixed-gas thermal-hydraulics within the canister and use data obtained from scale-model canister leakage experiments, as taught by Li, in the modified Takeda’s method for the predictable purpose of enhancing modeling, accuracy, and reliability as suggested by Li.
Regarding claim 26, Takeda in view of Martin and Li teaches the method of claim 25. Takeda discloses the alarm is generated when a bottom-to-top exterior surface temperature difference exceeds a threshold ([0072]), but does not appear to disclose a specific value of the threshold value. However, it would have been obvious to a POSA to have a threshold value that is two degrees Celsius since it has been held that, where the general conditions of a claim are disclosed in the prior art, discovering an optimum or workable range involves only routine skill in the art. Takeda explicitly discloses the threshold value may be determined via simulations ([0047]).
Regarding claim 27, Takeda in view of Martin and Li teaches the method of claim 25. Takeda discloses the plurality of exterior surface locations includes a top-center exterior surface location (4TC) of the canister and a bottom-center exterior surface location (4BC) of the canister (FIGS. 1-2, 13-15, [0048], [0058]-[0059]).
Regarding claim 28, Takeda in view of Martin and Li teaches the method as recited of claim 25. Takeda discloses responsive to generating the alarm, determining an indication of a chemical change within the canister comprising an increase in gaseous moiety and air mixture concentrations ([0052]-[0054], [0056], [0072]).
Regarding claim 29, Takeda in view of Martin and Li teaches the method of claim 28. Takeda discloses the gaseous moiety comprises an element selected from the group consisting of helium, krypton, xenon, hydrogen, nitrogen, argon and combinations thereof ([0052]-[0054]).
Regarding claim 30, Takeda in view of Martin and Li teaches the method of claim 25. Takeda discloses the temperatures at the plurality of exterior locations are repeatedly measured (FIGS. 13-15), but appears to be silent as to the specific time interval. However, it would have been obvious to a POSA to have a time interval in the range of 60 seconds since it has been held that, where the general conditions of a claim are disclosed in the prior art, discovering an optimum or workable range involves only routine skill in the art. A POSA would have been aware that a lower time interval would result in more detailed temperature information, but increased computational and storage requirements, while a greater time interval would result in less detailed temperature information, but decreased computational and storage requirements.
Regarding claim 31, Takeda in view of Martin and Li teaches the method of claim 25. Takeda discloses monitoring, by a sensor monitor module (18), a plurality of physical sensors (13A, 13B, 13C, 13E) configured to measure the temperatures at the plurality of exterior surface locations (FIG. 3, [0050], [0068]-[0069]); receiving, by a communication module (16a), sensor information from a sensor monitor module (18), sensor information from the sensor monitor module (FIG. 3, [0068]-[0069]); and receiving, by a data server (17), the sensor information from the communication module (FIG. 3, [0068]-[0069]).
Response to Arguments
Applicant’s amendments to the specification overcome the prior specification objection.
Applicant’s amendments to the claims overcome the prior 35 U.S.C. 112(b) rejections, but have created new issues as discussed above.
Applicant’s arguments directed towards the prior art rejections have been fully considered, but are directed towards newly added and/or amended claim language and are therefore addressed in the rejections above.
Additional References
The following references would also appear to be relevant to Applicant’s invention and are therefore cited in the attached PTO-892:
“CFD analysis of a cask for spent fuel dry storage” (Abstract), “Development of a Device for Detecting Helium Leaks from Canisters (Part 2)” (Abstract), “Development of Device for Detecting Helium Leak from Canister” (pp. 1-2), “Monitoring Helium Integrity in Welded Canisters” (Abstract): disclose a method for detecting a helium gas leak in a sealed spent-fuel canister comprising measuring temperatures at a plurality of exterior surface locations of the canister, using computational fluid dynamics simulation of mixed-gas heat transfer within the canister, and using temporal-spatial temperature data obtained and validated by scale-model canister leakage experiments
“Aging Management for Extended Storage and Transportation of Used Nuclear Fuel” (pp. 243-244, 246-247): discloses a method for detecting a helium gas leak in a sealed spent-fuel canister and using computational fluid dynamics simulation of mixed-gas heat transfer within the canister in order to benchmark calculated temperatures against measured data and provide insight into temperature profiles of components in the spent fuel cask
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Prosecution on the merits is closed. See MPEP 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
RCE Eligibility
Since prosecution is closed, this application is now eligible for a request for continued examination (RCE) under 37 CFR 1.114. Filing an RCE helps to ensure entry of an amendment to the claims, specification, and/or drawings.
Interview Information
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Contact Information
Examiner Jinney Kil can be reached at (571) 270-5217, on Monday-Thursday from 8:30AM-6:30PM ET. Supervisor Jack Keith (SPE) can be reached at (571) 272-6878.
/JINNEY KIL/Examiner, Art Unit 3646
Prosecution Insights